HN user
It depends. In music for example it's often question whether the artist has been exposed to the original work. In that spirit, small language models are less likely to infringe copyright.
It was not really a traditional rewrite. More like transpilation. Still impressive
I don't like the copy paste. Rather use lower-level abstractions for the UI if I know I have to make modifications.
You need to understand the bits you are trying to prove, but not the full proof. It's more like reading haskell types than math, even though the vocabulary is heavily inspired by math.
Is there benefit of using this branded type over just encapsulating the raw string in a private variable in closure or class? This feels a bit like forced nominal typing. The Email type doesn't have to be a string, it can be encapsulated so that invalid Emails are not representable.
Over the last couple months I've seen the debt multiplication in some OSS projects. It's like premature aging of a codebase.
"before Bison version 1.24, Bison-generated parsers could be used only in programs that were free software."
https://www.gnu.org/software/bison/manual/html_node/Conditio...
Likely just that they err on the unnecessary error handling than missing error handling. They likely penalize runtime errors harshly in the training
There are codebases out there with enormous amounts of duplication, filled with implicit dependencies. You just haven't encountered them to appreciate good abstraction.
The author has huge online following, and influence over developers. I don't doubt the author is genuinely interested in this, but the employer definitely is supporting this too.
Bingo. Dozen LLM-prototypes and then a manmade final patch which is merged.
This is mainly for performance and memory layouts, it would not have improved safety guarantees of java.
Curl has a ton of features, I can imagine this means fixing small fraction of the vulns affecting only the supporters.
The spec and proof are separate. In this blog article he mentions seL4 formal verification, where they state that the spec was 4900 lines of Isabelle and the proof was 200K lines. Obviously human has to understand the spec deeply.
He mentions the seL4 microkernel. The specification is written in Isabelle, and it's relatively complex: PDF https://sel4.systems/Info/Docs/seL4-spec.pdf
The bottleneck seems to be that clearly it's critical to be certain about the spec.
Maybe not for kernels, but I can see use for cryptographic algorithms, etc?
Claude fix outage. No mistakes.
I think it's part of their marketing. Anthropic is not really ahead of other labs but these releases make it seem like they are reaching singularity
Seems to me this is more honest than the Mythos claims a while ago. too powerful to release publicly. Too expensive?
Prototyping is widely underappreciated. People think it's waste to throw away stuff but it's more costly to build upon shoddy foundations
Ocaml is probably closest to that. The ML language family has roots in generic code with global type inference.
The ranking is not comparable across time like that.
I was thinking maybe the author really likes Datadog MCP or has some kind of conflict of interest. It's weird to see this content in HN.
Maybe not. Sqlite has some kind of hand-written license-agreement waiver procedure.
I'm not sure if the authors means "ai engineer" like that.
Theoretically it mandates that derivative works use same license but it's unclear if that applies to LLM outputs.
jumping back and forth between companies
There is currently quite strong incentive to establish vendor lock-in for Anthropic and OpenAI. I see the ability to jump between companies quite important, especially for larger users. Right now it should not be hard but it can be much harder in the future.
FYI many of the tsz sound mode features are available as linter plugins for typescript. They leverage type information
Let's start with laws which mandates that copyleft in LLM inputs transfer to LLM outputs
My guess is just open source maintainers trying out new genAI tools out of curiosity. Unintentional slopification
I have used AI agents extensively for coding and my experience is that it's fine for prototypes, but in large projects like this there is risk that the codebase becomes unmaintainable.